1. Field of the Invention
The present invention relates to a characteristic adjusting method in a process of manufacturing products.
2. Description of the Related Art
A large number of steps are incorporated into a process of manufacturing products, and a step of adjusting a characteristic is provided in the process of manufacturing products. When a processing condition for controlling a characteristic of products is imparted into the step of adjusting a characteristic thereof, the characteristic can be often incorporated into the products when the products are finished. For example, in the manufacturing process shown in FIG. 6, after steps 1 to nxe2x88x921 have been successively executed, the processing condition for controlling a characteristic is imparted into step n which is a step for adjusting a characteristic. Further, after step n+1 etc. have been successively executed, the characteristic of the finished product is inspected. For example, in the case of ceramic products, heat treatment corresponds to the process for adjusting a characteristic, and the heat treatment condition in the step of heat treatment corresponds to the processing condition for controlling a characteristic. In this case, the resultant characteristic obtained in characteristic inspection is fed back to the characteristic adjusting step, so that the processing condition for controlling a characteristic is changed in the step and the resultant characteristic in the characteristic inspection is made to agree with a target value.
However, according to the above method, following problems may be encountered. The resultant product characteristic that does not agree with a target value is confirmed in a final characteristic inspection step, and a correcting action is started after the confirmation in the final characteristic inspection step. When a lead time exists between the characteristic adjusting step (step n) and the characteristic inspection step, all products, which have been produced in a time period corresponding to the lead time, do not agree with the target value.
On the other hand, and different from the above method in which a resultant characteristic obtained in a characteristic inspection is fed back to the processing condition for controlling the characteristic, a method of predicting the product characteristic is disclosed in Japanese Unexamined Patent Publication Nos. 10-187206 and 5-204407. For example, according to the technique disclosed in Japanese Unexamined Patent Publication No. 10-187206, a process is predicted on the basis of a mathematical model (theoretical formula) simulating the process of manufacturing a product body to be processed. In this case, in order to enhance the prediction accuracy, an offset value is found so as to correct the mathematical model on the basis of actual processing result data, obtained in the past, which are grouped according to the type of the product body to be processed or according to the process condition. However, in the case of the above patent publication, the mathematical model (theoretical formula) is required for predicting the result of process. Therefore, unless the mathematical model is made with high accuracy, it is impossible to predict with high accuracy. Further, it can be considered that a mathematical model of a manufacturing process can not be made. This technique disclosed in the above patent publication cannot be applied to a manufacturing process, a mathematical model of which cannot be made.
According to the technique disclosed in Japanese Unexamined Patent Publication No. 5-204407, a quantity of state and a quantity of output are determined to be variables to express a state of the process, and sampling is conducted on the quantity of state and the quantity of output in a specific time period. Multiple regression analysis is conducted in the multiple regression analysis section by using sampling values from a point of time of each sampling to a point of time of a predetermined time period in the past, so that a coefficient of partial regression can be found. Then, when patterns of the coefficients of partial regression are classified by a neural network, the pattern of change in the output value of the process is predicted. However, according to the above method, the output characteristic itself of the process (system) is not predicted but only the pattern of a change is predicted. Therefore, it has been impossible to conduct a fine adjustment, that is, it is impossible to incorporate a desired characteristic into a final characteristic of the product.
The present invention has been accomplished to solve the above problems. It is an object of the present invention to provide a characteristic adjusting method in a process of manufacturing products so that a characteristic can be relatively easily incorporated into products and the accuracy of incorporation of a characteristic can be enhanced.
In the manufacturing process according to the present invention, a characteristic adjusting step for imparting a processing condition for controlling a characteristic is executed in a large number of steps, and a characteristic inspecting step is executed via at least an another step after the characteristic adjusting step. In the characteristic adjusting method of the present invention, when a general classification is made, the major steps consist of a data preparing stage, a model making stage and a model applying stage. In more detail, in the data preparing stage, a set of data, for each product lot, which includes: respective intermediate characteristics obtained in each step before the characteristic adjusting step; a processing condition for controlling the characteristic imparted in the characteristic adjusting step; and a product characteristic, in the characteristic inspecting step, obtained on the basis of the intermediate characteristics in respective steps and the processing condition for controlling the characteristic, is prepared. In the model making stage, a learning model, which expresses a causal relation when the intermediate characteristic and the processing condition for controlling the characteristic are inputted and the product characteristic is outputted, is made by using the sets of data prepared in the above stage. Further, in the model applying stage, in the characteristic adjusting step, the most appropriate processing condition for controlling the characteristic is retrieved from the intermediate characteristics obtained in the steps before the characteristic adjusting step by using the learning model made in the above stage. In the second aspect of the present invention, in this model applying stage, on the assumption of the intermediate characteristics obtained in the steps before the characteristic adjusting step, product characteristics are predicted by changing the processing condition for controlling the characteristic and a processing condition for controlling a characteristic, which is predicted to create small error in a product characteristic, is retrieved.
After all, the intermediate characteristics and the processing condition for controlling the characteristic are important factors to determine the product characteristic. Therefore, when the learning model is made from the causal relation of the intermediate characteristics and the processing condition for controlling the characteristic with the product characteristic in the characteristic inspection step, as described above and, further, when the learning model is applied to the process after that, the product characteristic, in each product manufacturing, with respect to the intermediate characteristics and the processing condition for controlling the characteristic, in each product manufacturing, can be precisely predicted. In other words, in the characteristic adjusting step, the processing condition for controlling the characteristic to obtain a desired product characteristic can be automatically and appropriately retrieved from the intermediate characteristics (the processing result of products) obtained in the step before the characteristic adjusting step. In this case, different from the method disclosed in the above patent publication, incorporation of the product characteristic into products can be easily realized without using a mathematical model (theoretical formula). As the characteristic can be predicted in anticipation of the final product characteristic, incorporation of the product characteristic can be conducted with high accuracy.
According to the conventional method in which the inspection result obtained in the characteristic inspecting step is fed back to the characteristic adjusting step each time, other steps are provided between the characteristic adjusting step and the characteristic inspecting step. Therefore, a time delay is necessarily caused when the characteristic is incorporated into the product. However, according to the present invention, the product characteristic is predicted by the learning model. Therefore, incorporation of the characteristic into the product can be executed without causing a time delay.
In this specification, the processing condition for controlling the characteristic to be imparted in the characteristic adjusting step is defined as a processing condition which affects the final product characteristic. A typical processing condition for controlling a characteristic is a condition of heat treatment conducted on ceramic products etc. In a series of manufacturing process, a step having a great influence on a product characteristic is recognized as a characteristic adjusting step.
In the third aspect of the present invention, each set of data are plotted in a multi-dimensional space by parameters of the intermediate characteristics and the processing condition for controlling a characteristic for each set of data prepared in the data preparing stage, each set of data are classified into a plurality of clusters and a new representative point is calculated from an average of data of the same cluster. A learning model is made by using the representative point, which has been calculated as described before, in the model making stage. In this case, it is possible to provide both the effect in which deviation of a data distribution is corrected and the effect in which noise is reduced by the averaging processing. As a result, the accuracy of approximation of the learning model can be enhanced. Accordingly, the prediction accuracy of the product characteristic can be enhanced. Further, the processing time for learning stage can be reduced by the data compression effect.
In the fourth aspect of the present invention, concretely as a specific cluster processing (classifying the data into clusters), a maximum distance between any two data of each set of data in the multi-dimensional space is calculated, and the cluster processing is preferably conducted in the range of X% of the maximum distance.
In the fifth aspect of the present invention, the model making stage is constructed by a neural network. In this case, when the causal relation is estimated by appropriately combining a large number of inputs and outputs, it is possible to obtain a learning model of high accuracy in a short period of time.
In the sixth aspect of the present invention, when a predetermined number of sets of data are accumulated in the usual manufacturing process, the learning model is renewed at the point of time in the data preparing stage and the model making stage. In the manufacturing process, a process state is changed for various factors. Accordingly, there is a possibility that the learning model made in the past has not been the most appropriate model. In this case, when the learning model is renewed, if necessary, even if the process state etc. are changed, the learning model can be optimized corresponding to the change.
In the seventh aspect of the present invention, when a new learning model is made, the newest learning model and the learning model which has already been adopted at least at the present time, are compared with each other, and the learning model, which is predicted to create smallest error in a product characteristic, is determined to be a learning model to be adopted hereinafter. Due to the foregoing, incorporation of the product characteristic can be more preferably executed.
In the eighth aspect of the present invention, the present invention can be preferably applied to a process of manufacturing a ceramic gas sensor. The ceramic gas sensor detects a specific component concentration in gas to be detected. Therefore, a solid electrolyte layer, an electrode layer and a protective layer of the sensor element are made in respective steps. After that, in the characteristic adjusting step, a heat treatment condition is set as a processing condition for controlling a characteristic. In the characteristic inspecting step, an output characteristic of the sensor element is inspected. In this manufacturing process of manufacturing the gas sensor, in the model applying stage, the heat treatment condition is changed on the assumption of the intermediate characteristics obtained in steps before the characteristic adjusting step, and the heat treatment condition to obtain a desired sensor output characteristic is retrieved. In this case, the sensor output characteristic is changed by the intermediate characteristics and the processing condition (heat treatment condition) for controlling a characteristic. However, according to the present invention described above, the sensor output characteristic can be always adjusted within the range of the standard. As a result, the manufacture of defective gas sensors can be prevented, and the quality of the gas sensors can be improved.
The present invention may be more fully understood from the description of the preferred embodiments of the invention set forth below, together with the accompanying drawings.